A Zernike Moment based methodology for heart disease detection

Author

Das, Abhyuday

Author_Institution

Technol. Integration, Deloitte Touche Tohmatsu India Private Ltd., Mumbai, India

fYear

2012

fDate

19-20 Oct. 2012

Firstpage

1

Lastpage

4

Abstract

This paper details a Zernike Moments and Fuzzy-C-Means clustering based technique to identify the nature of an ECG image. The proposed method can detect whether the ECG image belongs to a normal heart or a diseased heart. In the second case it can indicate the disease of the heart also. The method has been tested on four databases- congestive heart failure database, ventricular tachyarrhythmia database, atrial fibrillation database and normal sinus rhythm database. The experiment shows that the proposed technique is successful in 98.7% cases.

Keywords

cardiology; diseases; electrocardiography; fuzzy set theory; medical image processing; pattern clustering; ECG image identification; Zernike moment-based methodology; atrial fibrillation database; congestive heart failure database; diseased heart; electrocardiography; fuzzy-c-means clustering-based technique; heart disease detection; normal heart; normal sinus rhythm database; ventricular tachyarrhythmia database; Computers; Databases; Electrocardiography; Feature extraction; Heart; Image recognition; Training; Computer Aided Diagnosis; Fuzzy C-Means Clustering; Zernike Moment;

fLanguage

English

Publisher

ieee

Conference_Titel

Communication, Information & Computing Technology (ICCICT), 2012 International Conference on

Conference_Location

Mumbai

Print_ISBN

978-1-4577-2077-2

Type

conf

DOI

10.1109/ICCICT.2012.6398224

Filename

6398224